Instructions to use hf-tiny-model-private/tiny-random-IBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-IBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-IBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-IBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-IBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 394c2306e111a3958d58949aa8df6462cf41f99ad6e378c2b3e99e57e485c407
- Size of remote file:
- 726 kB
- SHA256:
- 176f0dbe000d3c0d27b859ae46cf12c846ce15cf720d4fb96c25c256c0cda5f9
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